We’ve all been there – stuck on a project, feeling frustrated, and wondering if it’s worth the hassle. That’s exactly what happened to me when I tried to upscale an old video of a talk show using machine learning (ML) image super-resolution.
I’m not a computer vision or image processing expert, but I know the basics of Python and Linux. I thought, how hard could it be, right? I’d use ChatGPT to piece together a quick script, and voilà! But, oh boy, was I wrong.
Four hours of trying to fix compatibility issues with torch, numpy, and ESRGAN versions later, I was left feeling angry and frustrated. It was like banging my head against a wall. Google and Stack Overflow were more helpful than ChatGPT, but I still didn’t get the results I wanted.
That’s when I realized I needed a roadmap to learn ML image super-resolution and upscaling. I didn’t want to enroll in a MIT-level course on linear algebra, but I needed to understand the basics.
So, I turned to Google Colab, a free online platform for data science and ML. It’s not perfect, but it’s a great starting point for beginners like me.
Here’s what I learned:
## Start with the basics
Don’t try to run before you can walk. Learn the fundamentals of image processing and computer vision. There are plenty of online resources, including tutorials and courses on Coursera and edX.
## Choose the right tools
Google Colab is a great platform for beginners, but you may need to upgrade to a more powerful GPU or AI rig for larger projects.
## Practice, practice, practice
The best way to learn is by doing. Start with simple projects, like upscaling images, and gradually move to more complex tasks.
## Join online communities
Reddit’s r/MLQuestions and other online forums are great places to ask questions, share knowledge, and learn from others.
In the end, upscaling my old video was just a small project, but it taught me a valuable lesson: learning ML image super-resolution and upscaling takes time, patience, and practice. Don’t be discouraged by setbacks – just keep learning and trying.
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*Further reading: [Image Super-Resolution with Deep Learning](https://towardsdatascience.com/image-super-resolution-with-deep-learning-2e8a3e1a8f5f)*